This course on Convolutional Neural Networks aims to teach learners about the fundamentals of computer vision, including tasks such as image recognition, object localization, and event prediction. The course covers topics such as manual feature extraction, learning feature representations, convolutional layers, and end-to-end frameworks for various applications. The teaching method includes lectures and practical examples, making it suitable for individuals interested in deep learning, computer vision, and artificial intelligence.
Overview
Syllabus
Intro
To discover from images what is present in the world, where things are, what actions are taking place, to predict and anticipate events in the world
The rise and impact of computer vision
Impact: Self-Driving Cars
Impact: Medicine, Biology, Healthcare
Images are Numbers
Tasks in Computer Vision
Manual Feature Extraction
Learning Feature Representations Can we learn a hierarchy of features directly from the data instead of hand engineering
Fully Connected Neural Network
Using Spatial Structure
Feature Extraction with Convolution
Filters to Detect X Features
The Convolution Operation
Producing Feature Maps
Convolutional Layers: Local Connectivity
Introducing Non-Linearity
Pooling
Putting it all together
An Architecture for Many Applications
Classification: Breast Cancer Screening
Semantic Segmentation: Fully Convolutional Networks
Continuous Control: Navigation from Vision
End-to-End Framework for Autonomous Navigation
Deep Learning for Computer Vision: Summary
Taught by
https://www.youtube.com/@AAmini/videos
